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UniRig-Ai on Replicate takes a 3D mesh and predicts a skeleton and skinning weights, producing a rigged model you can download. The hosted route is the easiest way to try it; the result still needs inspection because missing or inaccurate bones can lead to poor deformation.
What UniRig-Ai does
Rigging makes a 3D mesh poseable. A skeleton defines the bones and their hierarchy; skinning weights determine how strongly each bone influences each mesh vertex. UniRig predicts both, in separate stages, for an input model. The Replicate listing for aaronjmars/unirig-ai wraps the UniRig framework as a hosted model with one mesh input and a downloadable file output. Replicate’s model listing
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Automatic rigging is a starting point, not a promise of production-ready animation. The skeleton must suit the character or object, and the weights must make its surface deform plausibly when joints move.
Which mesh formats can you use?
Replicate documents .glb, .obj, .fbx, and .vrm as accepted input formats. Its published example uses a GLB file and returns a downloadable GLB. Check the live model page for current input requirements before uploading, as the hosted wrapper’s schema is the authority for what it accepts. Replicate’s API schema and examples
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How to try UniRig-Ai on Replicate
- Prepare a supported mesh. Choose a file in one of the documented formats and make sure you have the right to upload and use it. The model page’s input field is named
input_mesh. - Run it in the hosted playground. Open the Replicate model page, provide the mesh, and start a run. The service handles the model execution on its hosted hardware.
- Download the result. When the run finishes, retrieve the output file from the result page. The documented example returns a GLB, but confirm the actual output format and file before building it into another workflow.
- Inspect the rig before using it. Open the output in a 3D application, check that expected bones are present, and pose key joints to see how the mesh deforms. Look for detached or missing bones, collapsing joints, and unwanted stretching or pinching.
What to check in the result
Skeleton completeness
Confirm that the rig contains the bones needed for the asset. A missing tail or wing bone is not just a cosmetic issue: skinning can degrade substantially when the predicted skeleton is inaccurate. The UniRig project recommends correcting the skeleton before proceeding to skinning when necessary. Official UniRig repository and README
Skin deformation
Pose representative joints rather than judging the rig only in its neutral position. Check bends, extremities, and areas where limbs meet the torso. A plausible skeleton does not guarantee good weights, and the weights can expose problems that were not obvious in the bone layout.
Rank #2
Fit for your asset
How much cleanup is needed depends on the mesh and intended use. The cited project materials do not establish a wrapper-wide success rate or benchmark across asset types, so do not treat one successful output—or a paper result—as a guarantee for another input.
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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Replicate or local installation?
Replicate avoids local dependency and GPU setup, making it the more direct option for a first trial. Local execution gives technically comfortable users more control over the documented prediction and review steps, but requires a suitable Python and CUDA environment.
Rank #3
| Consideration | Replicate hosted model | Local UniRig project |
|---|---|---|
| Setup | Upload a mesh through the playground, or call the API with a Replicate token. | Install the project dependencies and run the documented inference workflow. |
| Compute | Replicate lists an A100 with 80 GB of memory for hosted execution. | The README specifies a CUDA-enabled GPU with at least 8 GB VRAM for generation. |
| Cost and time | The listing reports an approximate $0.46 per run and says runs typically finish within six minutes; both vary with the input and are not guarantees. | The cited README does not state a comparable per-run cost or runtime. |
| Control | Convenient for a straightforward run; API use supports programmatic access. | The documented workflow separates skeleton prediction, skeleton review, skinning prediction, and merging. |
Replicate’s displayed cost and runtime are volatile estimates; verify the live listing before running a job. API examples are available in Python, JavaScript, and HTTP, and require a Replicate token. Replicate API documentation for this model
What local use requires
The official repository specifies Python 3.11, PyTorch 2.3.1 or later, project dependencies, and a CUDA-enabled GPU with at least 8 GB of VRAM for generation. Its README also warns that Flash Attention installation errors are common. Optional VRM import and export uses a modified Blender VRM add-on; consult the repository for its installation details and current instructions. UniRig repository setup and inference instructions
Rank #4
The local inference sequence is designed to let you review the skeleton before skinning. The README distinguishes skeleton-only output from the skinning result and explains how to merge the result with the original mesh. Follow those project instructions for the applicable files and commands rather than assuming the hosted one-step output has identical controls.
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The UniRig repository identifies the work as a SIGGRAPH 2025 project and cites the paper “One Model to Rig Them All: Diverse Skeleton Rigging with UniRig.” The repository describes a progressive release: skeleton and skinning prediction code and a checkpoint are listed as available, while a full checkpoint replicating the paper’s main results is described as planned. The paper’s findings apply to its evaluated setup; they are not a wrapper-specific measured success rate or a guarantee for every Replicate input. Official UniRig repository ACM paper record
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The repository links an MIT license for its code. That license alone does not establish the terms for every checkpoint, input asset, dataset, or generated output; check the relevant terms for each before using them commercially or redistributing them.
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